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A frequency-filtering strategy of obtaining PHI-free sentences from clinical data repository

Authors:

Majid Rastegar-Mojarad,

Ravikumar Komandur Elayavilli,

Hongfang LiuAuthors Info & Claims

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 315 - 324

https://doi.org/10.1145/2808719.2808752

Published: 09 September 2015 Publication History

Abstract

Clinical natural language processing (NLP) has become indispensable in the secondary use of electronic medical records (EMRs). However, it is found that current clinical NLP tools face the problem of portability among different institutes. An ideal solution to this problem is cross-institutional data sharing. However, the legal enforcement of no revelation of protected health information (PHI) obstructs this practice even with the availability of state-of-the-art de-identification tools. In this paper, we investigated the use of a frequency-filtering approach to extract PHI-free sentences utilizing the Enterprise Data Trust (EDT), a large collection of EMRs at Mayo Clinic. Our approach is based on the assumption that sentences appearing frequently tend to contain no PHI. This assumption originates from the observation that there exist a large number of redundant descriptions of similar patient conditions in EDT. Both manual and automatic evaluations on the sentence set with frequencies higher than one show no PHI are found. The promising results demonstrate the potential of sharing highly frequent sentences among institutes.

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Cited By

(2034)Title Pending 320Journal of the Society for Clinical Data Management10.47912/jscdm.320Online publication date: 2-Jul-2034
https://doi.org/10.47912/jscdm.320
Mahendran DLuo CMcinnes B(2021)Review: Privacy-Preservation in the Context of Natural Language ProcessingIEEE Access10.1109/ACCESS.2021.31241639(147600-147612)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124163
Wang YFu SShen FHenry SUzuner OLiu H(2020)Overview of the 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity (Preprint)JMIR Medical Informatics10.2196/23375Online publication date: 10-Aug-2020
https://doi.org/10.2196/23375
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A frequency-filtering strategy of obtaining PHI-free sentences from clinical data repository

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cover image ACM Conferences

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

September 2015

683 pages

ISBN:9781450338530

DOI:10.1145/2808719

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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New York, NY, United States

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Published: 09 September 2015

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BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine

September 9 - 12, 2015

Georgia, Atlanta

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BCB '15 Paper Acceptance Rate 48 of 141 submissions, 34%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

(2034)Title Pending 320Journal of the Society for Clinical Data Management10.47912/jscdm.320Online publication date: 2-Jul-2034
https://doi.org/10.47912/jscdm.320
Mahendran DLuo CMcinnes B(2021)Review: Privacy-Preservation in the Context of Natural Language ProcessingIEEE Access10.1109/ACCESS.2021.31241639(147600-147612)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124163
Wang YFu SShen FHenry SUzuner OLiu H(2020)Overview of the 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity (Preprint)JMIR Medical Informatics10.2196/23375Online publication date: 10-Aug-2020
https://doi.org/10.2196/23375
Mahajan DPoddar ALiang JLin YPrager JSuryanarayanan PRaghavan PTsou C(2020)Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training using Multi-Task Learning (Preprint)JMIR Medical Informatics10.2196/22508Online publication date: 31-Jul-2020
https://doi.org/10.2196/22508
Sadat MAziz MMohammed NPakhomov SLiu HJiang X(2019)A privacy-preserving distributed filtering framework for NLP artifactsBMC Medical Informatics and Decision Making10.1186/s12911-019-0867-z19:1Online publication date: 7-Sep-2019
https://doi.org/10.1186/s12911-019-0867-z

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